This algorithm is a good fit for two reasons:
1) my program keeps track of individual nodes seperately anyway (most neural networks and algorithms work with entire layers rather than individual nodes).
2) there's going to be both user-created nodes and training-created nodes in the system, and the last thing the user wants is for the training algorithm to f with the nodes that he put there by hand. So I need algorithms that can work without changing the net that's already there.
And as a side note, that paper is yet *another* connectionist-related development to come out of CMU. I'm looking at places to go for grad school, and the CNBC is definitely first on my list.